img2tensor 0.1.1

Unified image to tensor utility with streaming TFRecord support

img2tensor

A unified, high-performance utility to convert images into training-ready tensors for NumPy, PyTorch, and TensorFlow, or stream them directly into TFRecords.

img2tensor handles the standard deep learning data ingestion "papercuts": BGR/RGB swaps, memory layouts (NHWC vs NCHW), and dtype scaling in a single, lightweight function.

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🚀 Installation

pip install img2tensor

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📖 Usage

1. Single Image (In-Memory)

Returns a 3D tensor ($C, H, W$ for PyTorch).

import img2tensor

Returns: torch.Tensor of shape (3, 224, 224)

tensor = img2tensor.get_tensor("cat.jpg", tensor_type="pytorch")

2. Batch Loading (In-Memory)

Returns a 4D tensor ($N, H, W, C$ for NumPy/TF).

Returns: np.ndarray of shape (32, 224, 224, 3)

batch = img2tensor.get_tensor(list_of_paths, n_jobs=8)

3. Production Pipeline (TFRecord)

Writes to disk using a chunked streaming approach to save RAM.

img2tensor.get_tensor( img_paths=large_list_of_paths, output_format="tfrecord", tfrecord_path="dataset.tfrecord", n_jobs=12 )

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🛠 API Reference: get_tensor()

Inputs

Parameter	Type	Default	Description
img_paths	`str	Path	list`
tensor_type	str	"numpy"	Target framework: "numpy", "pytorch", or "tensorflow".
dtype	str	"float32"	Target type: "float32", "float16", "uint8". Floats are auto-scaled (1/255).
image_layer	str	"PIL"	Backend decoder: "PIL" or "CV2".
n_jobs	int	4	Number of threads for parallel decoding.
output_format	str	"tensor"	"tensor" (returns object) or "tfrecord" (writes to disk).
tfrecord_path	`str	Path`	None

Outputs

• Single Path Input: Returns a 3D Tensor ($H, W, C$ for NumPy/TF; $C, H, W$ for PyTorch).
• List Input: Returns a 4D Tensor ($N, H, W, C$ for NumPy/TF; $N, C, H, W$ for PyTorch).
• TFRecord Mode: Returns a dict with file metadata (path, sample count, dtype).

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🧠 Design Philosophy

NCHW vs NHWC

One of the most frequent bugs in Computer Vision pipelines is passing the wrong channel layout. img2tensor detects your framework and adjusts automatically:

• PyTorch: Returns $N \times C \times H \times W$ (and ensures memory is .contiguous()).
• NumPy/TF: Returns $N \times H \times W \times C$.

What it Does NOT Do (By Design)

• No Resizing: We believe silent resizing is dangerous as it introduces artifacts. If your images are inconsistent sizes, get_tensor will throw a ValueError identifying the offending file.
• No Augmentation: This is a pure loader. Use specialized libraries like albumentations or torchvision for data manipulation.
• No Heavy Dependencies: Using lazy imports, the library won't crash if you don't have TensorFlow installed but only use NumPy or Torch.

📄 License

MIT